Perceptive analysis of query by singing system through query excerption

Abstract

Query by Singing (QBS) is a Music Information Retrieval (MIR) system with small audio excerpt as query. The rising availability of digital music stipulates effective music retrieval methods. Further, MIR systems support content based searching for music and requires no musical acquaintance. Current work on QBS focuses mainly on melody features such as pitch, rhythm, note etc., size of databases, response time, score matching and search algorithms. Even though a variety of QBS techniques are proposed, there is a dearth of work to analyze QBS through query excerption. Here, we present an analysis that works on QBS through query execrpt. To substantiate a series of experiments are conducted with the help of Mel-Frequency Cepstrum Coefficients (MFCC), Linear Predictive Coefficients (LPC) and Linear Predictive Cepstral Coefficients (LPCC) to portray the robustness of the knowledge representation. Proposed experiments attempt to reveal that retrieval performance as well as precision diminishes in the snail phase with the growing database size.